Machine Learning Practice Repository

This repository is dedicated to practicing key concepts from the Machine Learning course by Andrew Ng on Coursera. Each section focuses on solving problems related to the main topics covered in the course.

Topics

• Linear Regression
• Logistic Regression
• Neural Networks
• Unsupervised Learning
• Anomaly Detection
• Recommender Systems

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Folder Structure

machine-learning-practice/
├── data
│ ├── raw
│ ├── processed
│ └── README.md # Notes on datasets and preprocessing
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├── notebooks
│ └── ... # Organized by topic
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├── src
│ ├── preprocessing.py
│ ├── models.py # Code for building and training models
│ ├── utils.py
│ └── README.md # Notes on how to use scripts in `src/`
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├── results/ # Outputs of experiments (e.g., graphs, logs, model weights)
│ └── ... # Organized by topic
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├── reports/ # Summary reports for each problem or experiment
│ └── ... # Organized by topic
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├── tests/ # Unit tests for code in `src/`

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Linear Regression

Overview

Linear regression is a supervised learning algorithm used for predicting a target variable (y) based on a single feature (x) or multiple features (X). The goal is to minimize the error between the predicted and actual values by fitting a straight line.

Current Problem: Predicting Housing Prices

In this problem, we use a simple linear regression model to predict house prices based on one feature:

• Feature (x): Average number of rooms (RM)
• Target (y): House price (PRICE)

Steps

1. Understand the Data:

   • Use the Boston Housing dataset (RM vs. PRICE).

2. Build the Model:

   • Train a simple linear regression model using one feature (RM).

3. Evaluate the Model:

   • Calculate metrics like Mean Squared Error (MSE).
   • Visualize the results with a scatterplot and regression line.

4. Experiment:

   • Try another feature like LSTAT to see how it affects predictions.